Author:
(1) David Staines.
4 Calvo Framework and 4.1 Household’s Problem
4.3 Household Equilibrium Conditions
4.5 Nominal Equilibrium Conditions
4.6 Real Equilibrium Conditions and 4.7 Shocks
5.2 Persistence and Policy Puzzles
6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems
7 General Linearized Phillips Curve
8 Existence Results and 8.1 Main Results
9.2 Algebraic Aspects (I) Singularities and Covers
9.3 Algebraic Aspects (II) Homology
9.4 Algebraic Aspects (III) Schemes
9.5 Wider Economic Interpretations
10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs
10.4 Microeconomic Interpretation
Appendices
A Proof of Theorem 2 and A.1 Proof of Part (i)
B Proofs from Section 4 and B.1 Individual Product Demand (4.2)
B.2 Flexible Price Equilibrium and ZINSS (4.4)
B.4 Cost Minimization (4.6) and (10.4)
C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence
D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium
D.2 Profits and Long-Run Growth
E Slopes and Eigenvalues and E.1 Slope Coefficients
E.4 Rouche’s Theorem Conditions
F Abstract Algebra and F.1 Homology Groups
F.4 Marginal Costs and Inflation
G Further Keynesian Models and G.1 Taylor Pricing
G.3 Unconventional Policy Settings
H Empirical Robustness and H.1 Parameter Selection
I Additional Evidence and I.1 Other Structural Parameters
I.3 Trend Inflation Volatility
This section sketches the derivation of the Phillips curve log-linearized around any stochastic equilibrium. It rests on an application of two stochastic Grobman-Hartman theorems. Its formal justification will be supplied in the next two sections. The first part focuses on the slope coefficients and is supported by Appendix E, whilst the second solves out the structural errors around ZINSS. A full analysis of the properties of the error coefficients elsewhere is beyond the scope of this paper.
The surprising feature of this new linear approximation is that although it is certainty equivalent in deviations, this is not true of the whole system because the coefficients represent higher moments of the distributions. This novel aspect will allow this approximation to summarize critical aspects of the dynamical system. This approximation framework should prove a natural setting to analyze the dynamic and statistical properties models with idiosyncratic or large aggregate risks. This will require new computational and econometric routines, which I will touch on here.
The main quantitative work will focus on limiting cases comparable or in some cases equivalent to existing linearization designs. The solution will feature extensively manipulations with the lag operator. These will reappear prominently in the bifurcation analysis of Section 9, where I will also draw precise connections between terms in the Phillips curve and singularities like those discussed in Section 3.
This paper is available on arxiv under CC 4.0 license.